 Tuning parameter selection for penalized estimation via R2Julia C. Holter and Jonathan W. Stallrich Computational Statistics & Data Analysis, 2023, vol. 183, issue C Abstract: The tuning parameter selection strategy for penalized estimation is crucial to identify a model that is both interpretable and predictive. However, popular strategies (e.g., minimizing average squared prediction error via cross-validation) tend to select models with more predictors than necessary. A simple yet powerful cross validation strategy is proposed which is based on maximizing the squared correlation between the observed and predicted values, rather than minimizing squared error loss for the purposes of support recovery. The strategy can be applied to all penalized least-squares estimators and, under certain conditions, the metric implicitly performs a bias adjustment named the α-modification. When applied to the Lasso estimator, the α-modification is closely related to the relaxed Lasso estimator. The approach is demonstrated on a functional variable selection problem to identify optimal placement of surface electromyogram sensors to control a robotic hand prosthesis. Keywords: Cross validation; Model/variable selection; Functional data; Relaxed lasso (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:183:y:2023:i:c:s0167947323000403 DOI: 10.1016/j.csda.2023.107729 Access Statistics for this article Computational Statistics & Data Analysis is currently edited by S.P. Azen More articles in Computational Statistics & Data Analysis from Elsevier |
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